



'''simple docstring'''






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase:   str					=		False

class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    Any		,     __snake_case					:    List[str]=32      ):
      set_seed(0      )
      a		:   List[Any]	  =	UNetaDModel(sample_size=__snake_case		,     in_channels=3		,     out_channels=3      )
      a		:   int	  =	torch.optim.SGD(model.parameters()		,     lr=0.0001      )
      return model, optimizer







    @slow
    def      lowercase_			( self					:    Optional[int]      ):
      a		:   int	  =	'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
      a		:   Optional[Any]	  =	DDPMScheduler(
          num_train_timesteps=10_00		,     beta_start=0.0001		,     beta_end=0.02		,     beta_schedule='linear'		,     clip_sample=__snake_case		,     )
      a		:   List[str]	  =	DDIMScheduler(
          num_train_timesteps=10_00		,     beta_start=0.0001		,     beta_end=0.02		,     beta_schedule='linear'		,     clip_sample=__snake_case		,     )

      assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

      # shared batches for DDPM and DDIM
      set_seed(0      )
      a		:   Any	  =	[torch.randn((4, 3, 32, 32)      ).clip(-1		,     1      ).to(__snake_case      ) for _ in range(4      )]
      a		:   List[Any]	  =	[torch.randn((4, 3, 32, 32)      ).to(__snake_case      ) for _ in range(4      )]
      a		:   Tuple	  =	[torch.randint(0		,     10_00		,     (4,)      ).long().to(__snake_case      ) for _ in range(4      )]

      # train with a DDPM scheduler
      a  ,      a		:   List[Any]	  =	self.get_model_optimizer(resolution=32      )
      model.train().to(__snake_case      )
      for i in range(4      ):
        optimizer.zero_grad()
        a		:   Any	  =	ddpm_scheduler.add_noise(clean_images[i]		,     noise[i]		,     timesteps[i]      )
        a		:   List[str]	  =	model(__snake_case		,     timesteps[i]      ).sample
        a		:   Tuple	  =	torch.nn.functional.mse_loss(__snake_case		,     noise[i]      )
        loss.backward()
        optimizer.step()
      del model, optimizer

      # recreate the model and optimizer, and retry with DDIM
      a  ,      a		:   List[Any]	  =	self.get_model_optimizer(resolution=32      )
      model.train().to(__snake_case      )
      for i in range(4      ):
        optimizer.zero_grad()
        a		:   Any	  =	ddim_scheduler.add_noise(clean_images[i]		,     noise[i]		,     timesteps[i]      )
        a		:   str	  =	model(__snake_case		,     timesteps[i]      ).sample
        a		:   List[Any]	  =	torch.nn.functional.mse_loss(__snake_case		,     noise[i]      )
        loss.backward()
        optimizer.step()
      del model, optimizer

      self.assertTrue(torch.allclose(__snake_case		,     __snake_case		,     atol=1e-5      )      )
      self.assertTrue(torch.allclose(__snake_case		,     __snake_case		,     atol=1e-5      )      )




'''simple docstring'''






def  lowerCamelCase__			(		_A					,     _A					,     _A=False							):

  if isinstance(_A					,     _A							) and isinstance(_A					,     _A							):
    a		:   Tuple	  =	len(set_a.intersection(_A							)							)

    if alternative_union:
      a		:   Union[str, Any]	  =	len(_A							) + len(_A							)
    else:
      a		:   Optional[Any]	  =	len(set_a.union(_A							)							)

    return intersection / union

  if isinstance(_A					,     (list, tuple)							) and isinstance(_A					,     (list, tuple)							):
    a		:   int	  =	[element for element in set_a if element in set_b]

    if alternative_union:
      a		:   Optional[int]	  =	len(_A							) + len(_A							)
      return len(_A							) / union
    else:
      a		:   Tuple	  =	set_a + [element for element in set_b if element not in set_a]
      return len(_A							) / len(_A							)

    return len(_A							) / len(_A							)
  return None


if __name__ == "__main__":
     lowerCAmelCase:   str					=		{'a', 'b', 'c', 'd', 'e'}
     lowerCAmelCase:   Optional[Any]					=		{'c', 'd', 'e', 'f', 'h', 'i'}
     print(jaccard_similarity(set_a, set_b))